// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include <include/ocr_cls.h>

namespace PaddleOCR
{

    void Classifier::Run(std::vector<cv::Mat> img_list,
                         std::vector<int> &cls_labels,
                         std::vector<float> &cls_scores,
                         std::vector<double> &times)
    {
        std::chrono::duration<float> preprocess_diff = std::chrono::duration<float>::zero();
        std::chrono::duration<float> inference_diff = std::chrono::duration<float>::zero();
        std::chrono::duration<float> postprocess_diff = std::chrono::duration<float>::zero();

        int img_num = img_list.size();
        std::vector<int> cls_image_shape = {3, 48, 192};
        for (int beg_img_no = 0; beg_img_no < img_num;
             beg_img_no += this->cls_batch_num_)
        {
            auto preprocess_start = std::chrono::steady_clock::now();
            int end_img_no = std::min(img_num, beg_img_no + this->cls_batch_num_);
            int batch_num = end_img_no - beg_img_no;
            // preprocess
            std::vector<cv::Mat> norm_img_batch;
            for (int ino = beg_img_no; ino < end_img_no; ino++)
            {
                cv::Mat srcimg;
                img_list[ino].copyTo(srcimg);
                cv::Mat resize_img;
                this->resize_op_.Run(srcimg, resize_img, this->use_tensorrt_,
                                     cls_image_shape);

                this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
                                        this->is_scale_);
                if (resize_img.cols < cls_image_shape[2])
                {
                    cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0,
                                       cls_image_shape[2] - resize_img.cols,
                                       cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));
                }
                norm_img_batch.push_back(resize_img);
            }
            std::vector<float> input(batch_num * cls_image_shape[0] *
                                         cls_image_shape[1] * cls_image_shape[2],
                                     0.0f);
            this->permute_op_.Run(norm_img_batch, input.data());
            auto preprocess_end = std::chrono::steady_clock::now();
            preprocess_diff += preprocess_end - preprocess_start;

            // inference.
            auto input_names = this->predictor_->GetInputNames();
            auto input_t = this->predictor_->GetInputHandle(input_names[0]);
            input_t->Reshape({batch_num, cls_image_shape[0], cls_image_shape[1],
                              cls_image_shape[2]});
            auto inference_start = std::chrono::steady_clock::now();
            input_t->CopyFromCpu(input.data());
            this->predictor_->Run();

            std::vector<float> predict_batch;
            auto output_names = this->predictor_->GetOutputNames();
            auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
            auto predict_shape = output_t->shape();

            int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1,
                                          std::multiplies<int>());
            predict_batch.resize(out_num);

            output_t->CopyToCpu(predict_batch.data());
            auto inference_end = std::chrono::steady_clock::now();
            inference_diff += inference_end - inference_start;

            // postprocess
            auto postprocess_start = std::chrono::steady_clock::now();
            for (int batch_idx = 0; batch_idx < predict_shape[0]; batch_idx++)
            {
                int label = int(
                    Utility::argmax(&predict_batch[batch_idx * predict_shape[1]],
                                    &predict_batch[(batch_idx + 1) * predict_shape[1]]));
                float score = float(*std::max_element(
                    &predict_batch[batch_idx * predict_shape[1]],
                    &predict_batch[(batch_idx + 1) * predict_shape[1]]));
                cls_labels[beg_img_no + batch_idx] = label;
                cls_scores[beg_img_no + batch_idx] = score;
            }
            auto postprocess_end = std::chrono::steady_clock::now();
            postprocess_diff += postprocess_end - postprocess_start;
        }
        times.push_back(double(preprocess_diff.count() * 1000));
        times.push_back(double(inference_diff.count() * 1000));
        times.push_back(double(postprocess_diff.count() * 1000));
    }

    void Classifier::LoadModel(const std::string &model_dir)
    {
        paddle_infer::Config config;
        config.SetModel(model_dir + "/inference.pdmodel",
                        model_dir + "/inference.pdiparams");

        if (this->use_gpu_)
        {
            config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
            if (this->use_tensorrt_)
            {
                auto precision = paddle_infer::Config::Precision::kFloat32;
                if (this->precision_ == "fp16")
                {
                    precision = paddle_infer::Config::Precision::kHalf;
                }
                if (this->precision_ == "int8")
                {
                    precision = paddle_infer::Config::Precision::kInt8;
                }
                config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
                if (!Utility::PathExists("./trt_cls_shape.txt"))
                {
                    config.CollectShapeRangeInfo("./trt_cls_shape.txt");
                }
                else
                {
                    config.EnableTunedTensorRtDynamicShape("./trt_cls_shape.txt", true);
                }
            }
        }
        else
        {
            config.DisableGpu();
            if (this->use_mkldnn_)
            {
                config.EnableMKLDNN();
            }
            else
            {
                config.DisableMKLDNN();
            }
            config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
        }

        // false for zero copy tensor
        config.SwitchUseFeedFetchOps(false);
        // true for multiple input
        config.SwitchSpecifyInputNames(true);

        config.SwitchIrOptim(true);

        config.EnableMemoryOptim();
        config.DisableGlogInfo();

        this->predictor_ = paddle_infer::CreatePredictor(config);
    }
} // namespace PaddleOCR
